Short‐term effects of benchmarking on the manufacturing practices and performance of SMEs
Bibliographic record
Abstract
Facing increased competitive pressures due to globalisation and increased quality requirements from their customers, small and medium‐sized manufacturers must increase their productivity and their competitiveness in order to survive and prosper. One way of evaluating the attainment of this goal is to compare a firm's business practices and performance with those of a group of comparable firms, or with those of firms that are recognised for their excellence – that is, to “benchmark” the organisation. As management challenges have increased in complexity, benchmarking has become a strategic tool for organisations, both large and small, and for governments seeking to assist them. However, given a lack of empirical research, little is known as to the actual impacts of benchmarking. With this in mind, the present study sought to test a model of the relationship between benchmarking, the adoption of advanced manufacturing systems, and the performance of small to medium‐sized enterprises (SMEs). The model was tested with data from 102 Canadian manufacturing SMEs that have participated in a benchmarking exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".